3D single object tracking (3D SOT) in LiDAR point clouds is essential for autonomous driving. Most existing 3D SOT methods focus on clear weather, where point clouds are more defined. However, adverse weather conditions lead to sparser and noisier point clouds, significantly degrading tracking performance and posing safety risks. In this study, we introduce UAWTrack, a universal 3D SOT model designed to perform effectively across diverse real-world weather conditions. UAWTrack comprises three key modules: 1) Voxel Feature Extraction, which mitigates the perturbations in point clouds caused by adverse weather; 2) Motion-centric Spatial-temporal Aggregation and Motion-guided Feature Fusion, capturing motion clues and sampling dense BEV motion features to address the issue of sparsity; and 3) Weather-Specific Tracker, which efficiently handles tracking in various weather conditions. To fill the gap of lacking benchmarks for 3D SOT in adverse weather, we simulate physically valid adverse weather conditions on the KITTI and NuScenes datasets, creating two benchmarks: KITTI-A and NuScenes-A. Extensive experiments demonstrate that UAWTrack achieves state-of-the-art performance under all weather conditions.

3D Single Object Tracking (SOT) is a fundamental task in computer vision and plays a critical role in applications like autonomous driving. However, existing algorithms often involve complex designs and multiple loss functions, making model training and deployment challenging. Furthermore, their reliance on fixed probability distribution assumptions (e.g., Laplacian or Gaussian) hinders their ability to adapt to diverse target characteristics such as varying sizes and motion patterns, ultimately affecting tracking precision and robustness. To address these issues, we propose BEVTrack, a simple yet effective motion-based tracking method. BEVTrack directly estimates object motion in Bird's-Eye View (BEV) using a single regression loss. To enhance accuracy for targets with diverse attributes, it learns adaptive likelihood functions tailored to individual targets, avoiding the limitations of fixed distribution assumptions in previous methods. This approach provides valuable priors for tracking and significantly boosts performance. Comprehensive experiments on KITTI, NuScenes, and Waymo Open Dataset demonstrate that BEVTrack achieves state-of-the-art results while operating at 200 FPS, enabling real-time applicability. The code will be released at https://github.com/xmm-prio/BEVTrack.